Recommending documents sets based on a similar set of correlated features

ABSTRACT

A method for recommending a plurality of alternate search keywords is provided. The method may include executing a first search query using a plurality of user-entered search text. The method may also include identifying a highest contribution keyword to a plurality of search results of the executed first search query. The method may further include identifying a highest correlation alternate keyword to the identified highest contribution keyword. The method may also include creating an alternate keyword group by replacing the identified highest contribution keyword with the identified highest correlation alternate keyword. The method may further include executing a second search query using the created alternate keyword group. The method may also include displaying the plurality of search results associated with the executed first search query with a plurality of statistics associated with the executed second search query.

BACKGROUND

The present invention relates, generally, to the field of computing, andmore particularly to search query data processing.

A search engine is a program capable of searching for information acrossone or more databases. To assist in locating desired information, thesearch engine receives a user-entered search query. Typically, searchengines support informational queries, navigational queries, andtransactional queries. Once information related to the user-enteredsearch query is located, the search engine presents the locatedinformation to the user in a preconfigured order.

SUMMARY

According to one embodiment, a method for recommending a plurality ofalternate search keywords is provided. The method may include executinga first search query using a plurality of user-entered search text. Themethod may also include identifying a highest contribution keyword to aplurality of search results of the executed first search query. Themethod may further include identifying a highest correlation alternatekeyword to the identified highest contribution keyword. The method mayalso include creating an alternate keyword group by replacing theidentified highest contribution keyword with the identified highestcorrelation alternate keyword. The method may further include executinga second search query using the created alternate keyword group. Themethod may also include displaying the plurality of search resultsassociated with the executed first search query with a plurality ofstatistics associated with the executed second search query.

According to another embodiment, a computer system for recommending aplurality of alternate search keywords is provided. The computer systemmay include one or more processors, one or more computer-readablememories, one or more computer-readable tangible storage devices, andprogram instructions stored on at least one of the one or more storagedevices for execution by at least one of the one or more processors viaat least one of the one or more memories, whereby the computer system iscapable of performing a method. The method may include executing a firstsearch query using a plurality of user-entered search text. The methodmay also include identifying a highest contribution keyword to aplurality of search results of the executed first search query. Themethod may further include identifying a highest correlation alternatekeyword to the identified highest contribution keyword. The method mayalso include creating an alternate keyword group by replacing theidentified highest contribution keyword with the identified highestcorrelation alternate keyword. The method may further include executinga second search query using the created alternate keyword group. Themethod may also include displaying the plurality of search resultsassociated with the executed first search query with a plurality ofstatistics associated with the executed second search query.

According to yet another embodiment, a computer program product forrecommending a plurality of alternate search keywords is provided. Thecomputer program product may include one or more computer-readablestorage devices and program instructions stored on at least one of theone or more tangible storage devices, the program instructionsexecutable by a processor. The computer program product may includeprogram instructions to execute a first search query using a pluralityof user-entered search text. The computer program product may alsoinclude program instructions to identify a highest contribution keywordto a plurality of search results of the executed first search query. Thecomputer program product may further include program instructions toidentify a highest correlation alternate keyword to the identifiedhighest contribution keyword. The computer program product may alsoinclude program instructions to create an alternate keyword group byreplacing the identified highest contribution keyword with theidentified highest correlation alternate keyword. The computer programproduct may further include program instructions to execute a secondsearch query using the created alternate keyword group. The computerprogram product may also include program instructions to display theplurality of search results associated with the executed first searchquery with a plurality of statistics associated with the executed secondsearch query.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates an exemplary networked computer environment accordingto at least one embodiment;

FIGS. 2A and 2B are an operational flowchart illustrating an alternatekeyword recommendation process according to at least one embodiment;

FIG. 3 is an exemplary graphical user interface displaying keywordsearch query results according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

Embodiments of the present invention are related to the field ofcomputing, and more particularly to search query data processing. Thefollowing described exemplary embodiments provide a system, method, andprogram product to, among other things, provide search query results toa user along with alternate search keyword groups that includecorrelated words to the original user-entered search terms. Therefore,the present embodiment has the capacity to improve the technical fieldof search query data processing by performing search queries of keywordswords identified through machine learning as synonymous or closelyrelated to keywords within the user-entered search terms and calculatingstatistics related to the performed search queries.

As previously described, a search engine is a program capable ofsearching for information across one or more databases. To assist inlocating desired information, the search engine receives a user-enteredsearch query. Typically, search engines support informational queries,navigational queries, and transactional queries. Once informationrelated to the user-entered search query is located, the search enginepresents the located information to the user in a preconfigured order.

Recently, the practical use of machine learning has been implemented insearch engines. Machine learning may replace a conventional keywordsearch system based on a heuristic information acquisition technique. Inthe application of many supervised machine learning algorithms, manualcollection of data is required to gather information that is directlyrelevant to the user-entered keywords. Although searching allinformation within a corpus is unnecessary within such a system, acomprehensive search is desirable in order to detect as many correctcharacteristic patterns as possible.

For assisting in the collection of a question sentence (i.e. naturallanguage) and documents containing relevant information to theuser-entered search terms, a keyword-based search system may beutilized. In a keyword-based search, a list of answer candidates isanalyzed and, if appropriate, a particular search result may be flagged.If a search result is inappropriate, a flag may not be assigned to thatparticular search result. Data collection of search results may beconducted based on information assigned as appropriate to the search.Utilizing a search engine for collecting data allows for more efficientidentification of characteristic patterns among search results.

With respect to keyword searches, the connectors “and” and “or” may beused to further refine the results gathered by the search engine. Forexample, if two words in a keyword search are connected by the word“and,” the search engine may only return search results that contain thetwo words connected by the word “and.” Generally, use of keywordsconnected by the “and” condition increases the probability of eachsearch result being an appropriate search result. However, completenessof the search results is reduced since alternate responses are reduced.Conversely, use of keywords connected by the “or” condition increasesnoise (i.e. inappropriate responses) within the search results whileallowing for a greater field of search results. As such, it may beadvantageous, among other things, to implement a system that identifieskeywords related to, but not included within, the user-entered questionsentence to present to the user as alternate keyword groups to theuser-entered keywords.

According to one embodiment, a keyword search query may be interactivelypresented that leads to many appropriate search results being identifiedby a search engine. The contribution of each user-entered keyword may becalculated to identify which user-entered keywords appear in the mostsearch results. Additionally, a correlation value may be calculated foreach user-entered keyword within the keyword search. The higher-rankedkeyword groups may be used as features that represent a context of thesearch results. On a highest contribution to lowest contribution keywordbasis, the user-entered keywords may be replaced by the highestcorrelation value keywords. A subsequent search query may be executedfor each new keyword set that substitutes at least one user-enteredkeyword with a highly correlated keyword. Statistics, such as thecorrelation value of the subsequent keyword set to the user-enteredkeyword set and the multiplicity of the subsequent keyword set, may becalculated and displayed within a graphical user interface to a useralong with the search results of the user-entered search query.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following described exemplary embodiments provide a system, method,and program product to analyze a user-entered search query for keywordswith the highest contribution to the search results, determine highlycorrelated words to the user-entered keywords, and execute a subsequentsearch query where at least one user-entered keyword is substituted by ahighly correlated keyword.

Referring to FIG. 1, an exemplary networked computer environment 100 isdepicted, according to at least one embodiment. The networked computerenvironment 100 may include a client computing device 102 and a server112 interconnected via a communication network 114. According to atleast one implementation, the networked computer environment 100 mayinclude a plurality of client computing devices 102 and servers 112, ofwhich only one of each is shown for illustrative brevity.

The communication network 114 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. The communication network 114 may includeconnections, such as wire, wireless communication links, or fiber opticcables. It may be appreciated that FIG. 1 provides only an illustrationof one implementation and does not imply any limitations with regard tothe environments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The client computing device 102 may include a processor 104 and a datastorage device 106 that is enabled to host and run a software program108 and an alternate keyword recommendation program 110A and communicatewith the server 112 via the communication network 114, in accordancewith one embodiment of the invention. The client computing device 102may be, for example, a mobile device, a telephone, a personal digitalassistant, a netbook, a laptop computer, a tablet computer, a desktopcomputer, or any type of computing device capable of running a programand accessing a network. As will be discussed with reference to FIG. 4,the client computing device 102 may include internal components 402 aand external components 404 a, respectively.

The server computer 112 may be a laptop computer, netbook computer,personal computer (PC), a desktop computer, or any programmableelectronic device or any network of programmable electronic devicescapable of hosting and running an alternate keyword recommendationprogram 110B and a database 116 and communicating with the clientcomputing device 102 via the communication network 114, in accordancewith embodiments of the invention. As will be discussed with referenceto FIG. 4, the server computer 112 may include internal components 402 band external components 404 b, respectively. The server 112 may alsooperate in a cloud computing service model, such as Software as aService (SaaS), Platform as a Service (PaaS), or Infrastructure as aService (IaaS). The server 112 may also be located in a cloud computingdeployment model, such as a private cloud, community cloud, publiccloud, or hybrid cloud.

According to the present embodiment, the alternate keywordrecommendation program 110A, 110B may be a program capable ofcalculating a contribution value for each keyword in a search query fora set of search results, calculating a group of correlated keywords foreach keyword in a search query, substituting a user-entered keyword inthe search query with a highly correlated word, executing a subsequentsearch query, and calculating statistics that compare the results of theuser-entered search query to the results of the subsequent search query.The query recommendation method is explained in further detail belowwith respect to FIGS. 2A, 2B, and 3.

Referring now to FIGS. 2A, and 2B, an operational flowchart illustratingan alternate keyword recommendation process 200 is depicted, accordingto at least one embodiment. In FIG. 2A, at 202, the alternate keywordrecommendation program 110A, 110B receives a search sentence from auser. The search sentence received by the alternate keywordrecommendation program 110A, 110B may be a set of keywords or naturallanguage. For example, if a user is searching for a thin laptop made byIBM® (IBM and all IBM-based trademarks and logos are trademarks orregistered trademarks of International Business Machines Corporationand/or its affiliates), the received search sentence may be the naturallanguage sentence “I want to buy a thin IBM® laptop” or the keywords“thin IBM® laptop.”

Then, at 204, the alternate keyword recommendation program 110A, 110Bidentifies keywords within the search sentence. If search sentencereceived by the alternate keyword recommendation program 110A, 110B is anatural language sentence, the alternate keyword recommendation program110A, 110B may utilize known natural language processing techniques,such as automatic summarization, to identify keywords within the naturallanguage sentence. For example, if the received search sentence is thenatural language sentence “I want to buy a thin IBM® laptop,” thealternate keyword recommendation program 110A, 110B may implementnatural language processing techniques to identify the keywords of thesentence to be “thin,” “IBM®,” and “laptop.”

Next, at 206, the alternate keyword recommendation program 110A, 110Bexecutes a search query using the identified keywords as a keywordgroup. The keywords within the received search sentence may be groupedtogether by the alternate keyword recommendation program 110A, 110B intoa keyword group. Once the keywords are grouped together, the alternatekeyword recommendation program 110A, 110B may execute a search queryusing a search engine. As previously described, a search engine is aprogram capable of searching for information across one or moredatabases 116. For example, the alternate keyword recommendation program110A, 110B may utilize an internet search engine to execute a searchquery using the keywords “thin,” “IBM®,” and “laptop” as a keywordgroup.

Then, at 208, the alternate keyword recommendation program 110A, 110Breceives search results based on the executed query. Upon searching theone or more databases 116, the search engine may return search resultsrelevant to the keyword group used to execute the search query. Forexample, once a search for the keywords “thin,” “IBM®,” and “laptop” isexecuted by the internet search engine, the alternate keywordrecommendation program 110A, 110B may receive the results of that searchquery in various forms, such as text, images, or videos.

Next, at 210, the alternate keyword recommendation program 110A, 110Bcalculates a contribution value for each keyword to the search results.The alternate keyword recommendation program 110A, 110B may analyze eachkeyword within the search sentence to determine the contribution eachkeyword has in the received search results. Using known termfrequency-inverse document frequency (i.e., tf-idf) techniques, thealternate keyword recommendation program 110A, 110B may calculate acontribution score for each keyword in the keyword group used to executethe search query. Term frequency-inverted document frequency may relateto a standard for representing the importance of a particular termwithin a document of a corpus, such as a query search results. Termfrequency-inverted document frequency may be an effective tool indistinguishing highly relevant terms from irrelevant terms due to acontribution score, or value, calculated for each term within a searchkeyword group. According to one embodiment, the formula for calculatingthe contribution value for each keyword within the search keyword groupmay be represented as:(1+log(numDocs/docfreq+1))where numDocs may represent the number of documents (e.g., number oftotal search result hits) within the search results and docfreq mayrepresent the number of documents within the search results within whicha particular keyword appears.

Then, at 212, the alternate keyword recommendation program 110A, 110Bidentifies a highest contribution value keyword within the keyword groupsearch results. Once a contribution value is calculated for each keywordwithin the searched keyword group, the alternate keyword recommendationprogram 110A, 110B determines which keyword within the searched keywordgroup has the highest calculated contribution score. For example, if thekeyword group searched includes the keywords “thin,” “IBM®,” and“laptop” and the contribution score for each keyword is 40%, 35%, and25%, respectively, the alternate keyword recommendation program 110A,110B may identify the keyword “thin” as the keyword with the highestcontribution score within the search keyword group.

Next, at 214, the alternate keyword recommendation program 110A, 110Bidentifies alternate keywords closely related to or synonymous with thekeyword with the highest contribution score. A keyword used within theoriginal search query may have synonyms or other closely related wordswith similar meanings. The alternate keyword recommendation program110A, 110B may identify the synonymous or closely related words throughknown semantic analysis techniques, such as a thesaurus. Additionally,the alternate keyword recommendation program 110A, 110B may identify thesynonymous or closely related words through analyzing high frequencywords appearing in the search results of the executed search query. Forexample, a search query may be executed for the keyword group of “thin,”“IBM®,” and “laptop.” Within the search results, a document may containthe word “lightweight” in place of “thin.” Therefore, based on the useof the word “lightweight” within the context of the document, thealternate keyword recommendation program 110A, 110B may identify theword “lightweight” as being synonymous or closely related to the word“thin.”

Then, at 216, the alternate keyword recommendation program 110A, 110Bcalculates a correlation value for each alternate keyword. Thecorrelation value may be a similarity rating between a keyword withinthe keyword group and an alternate keyword for the identified keyword.Furthermore, the correlation value may be calculated as a comparisonbetween an observed value of the number of occurrences of a keywordwithin a set of search results and the expected, or estimated, number ofoccurrences of the keyword in a refined portion of the set of searchresults. In at least one embodiment, the formula for calculating thecorrelation value of an alternate keyword in a refined set of the searchresults may be:(measured_value)/(observed_value)×((num_ref_docs)/(total_doc_num))where measured_value may represent the number of occurrences of akeyword within a refined search result set, observed_value may representthe observed value of the keyword within the entire search result set,num_ref_docs may represent the size of the refined set of searchresults, and total_doc_num may represent the total size of the entiresearch result set.

Referring now to FIG. 2B, at 218, the alternate keyword recommendationprogram 110A, 110B identifies an alternate keyword with a highestcorrelation value. Once each alternate keyword has a calculatedcorrelation value (step 216), the alternate keyword recommendationprogram 110A, 110B may identify the alternate keyword with the highestcalculated correlation value. For example, if the keyword “thin” hasidentified alternate keywords of “lightweight,” “cool,” and “compact”with correlation values of 10.3, 7.2, and 3.9, respectively, thealternate keyword recommendation program 110A, 110B may identify thealternate keyword “lightweight” as alternate keyword with the highestcorrelation value.

Then, at 220, the alternate keyword recommendation program 110A, 110Bcreates an alternate keyword group. The alternate keyword recommendationprogram 110A, 110B may create the alternate keyword group by replacingthe highest contributing keyword with the highest correlation valuealternate keyword. For example, in the previous hypothetical, if thealternate keyword recommendation program 110A, 110B identified thehighest contributing keyword to a keyword group as the keyword “thin”and the highest correlation value alternate keyword to the keyword“thin” was identified as the alternate keyword “lightweight,” thealternate keyword recommendation program 110A, 110B may replace thekeyword “thin” with the alternate keyword “lightweight” in the keywordgroup. Therefore, an alternate keyword group would be created thatincludes the keywords “lightweight,” “IBM®,” and “laptop.”

Next, at 222, the alternate keyword recommendation program 110A, 110Bexecutes a second search query using the alternate keyword group.Similar to step 206, the alternate keyword recommendation program 110A,110B may execute a search query of the alternate keyword group using asearch engine. For example, the alternate keyword recommendation program110A, 110B may execute a search query using the keywords “lightweight,”“IBM®,” and “laptop” as the alternate keyword group.

Then, at 224, the alternate keyword recommendation program 110A, 110Breceives new search results based on the second search query. Similar tostep 208, the alternate keyword recommendation program 110A, 110B mayreceive search results relevant to the search query executed for thealternate keyword group. For example, once search for the alternatekeyword group of “lightweight,” “IBM®,” and “laptop” is executed by theinternet search engine, the alternate keyword recommendation program110A, 110B may receive the search results of the second search query invarious forms, such as text, images, or videos.

Next, at 226, the alternate keyword recommendation program 110A, 110Bcalculates a degree of approximation between the user-entered and thealternate keyword groups. The alternate keyword recommendation program110A, 110B may calculate a degree of approximation of highly correlatedwords between the original keyword group and the alternate keywordgroup. The degree of approximation of highly correlated words may berepresented on a 100 point scale where a higher value is associated withtwo highly correlated keyword groups. For example, if the alternatekeyword recommendation program 110A, 110B calculates a degree ofapproximation of 95 between a user-entered keyword group and analternate keyword group, the two keyword groups may be very highlycorrelated. The degree of approximation of highly correlated words maybe calculated in the same manner as the correlation value for each wordwithin the keyword group as previously described in step 216.

Then, at 228, the alternate keyword recommendation program 110A, 110Bcalculates a multiplicity value between the first and the second searchresults. The multiplicity value may relate to the similarity between thesearch results received from the first search query with theuser-entered keyword group and the search results of the second searchquery with the alternate keyword group. Similar to the degree ofapproximation of highly correlated words, the multiplicity value may berepresented on a 100 point scale where a higher value is associated withthe second set of search results having more identical search results tothe first set of search results. For example, if both the first set andsecond set of search results returned 100 items within each set and thealternate keyword recommendation program 110A, 110B calculated amultiplicity value of 95 between the two sets of search results, thenthe first set of search results and the second set of search resultsshare many identical documents within the respective search results.

Next, at 230, the alternate keyword recommendation program 110A, 110Bdisplays the first set of search results and a recommendation to searchthe alternate keyword group along with the degree of approximation ofhighly correlated words associated with the alternate keyword group andthe multiplicity value associated with the second set of search resultson a display screen. The alternate keyword recommendation program 110A,110B may present the first set of search results to the user through agraphical user interface on a display screen of the client computingdevice 102. Furthermore, the alternate keyword recommendation program110A, 110B may include a recommendation to the user to search thealternate keyword group. The recommendation to search the alternatekeyword group may include the degree of approximation of highlycorrelated words associated with the alternate keyword group and themultiplicity value associated with the second set of search results. Thedisplayed results are described in more detail below with respect toFIG. 3. For example, in the previously described situation, thealternate keyword recommendation program 110A, 110B may display thesearch results for the keyword group of “thin,” “IBM®,” and “laptop” toa user and include a recommendation to search the alternate keywordgroup of “lightweight,” “IBM®,” and “laptop” along with the calculateddegree of approximation of highly correlated words and the calculatedmultiplicity value associated with the alternate keyword group and theset of search results associated with the alternate keyword group.

Referring now to FIG. 3, an exemplary graphical user interfacedisplaying keyword search query results 300 is depicted, according to atleast one embodiment. When the alternate keyword recommendation program110A, 110B displays the keyword search query results 300 to the user,the keyword search query results 300 may be presented on a graphicaluser interface 302 of a display screen associated with a user device,such as client computing device 102. Within the graphical user interface302, the alternate keyword recommendation program 110A, 110B may alsodisplay the user-entered question sentence 304, the keyword search query306 executed by the alternate keyword recommendation program 110A, 110B,the search results 308 of the executed keyword search query 306, andrecommended alternate keyword search queries 310. As previouslydescribed, each alternate keyword search group within the recommendedalternate keyword search queries 310 may include a calculated degree ofapproximation of highly correlated words to the keyword search query 306and a calculate multiplicity value to the search results 308.

It may be appreciated that FIGS. 2A, 2B, and 3 provide only anillustration of one implementation and do not imply any limitations withregard to how different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

FIG. 4 a is a block diagram 400 of internal and external components ofthe client computing device 102 and the server 112 depicted in FIG. 1 inaccordance with an embodiment of the present invention. It should beappreciated that FIG. 4 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The data processing system 402, 404 is representative of any electronicdevice capable of executing machine-readable program instructions. Thedata processing system 402, 404 may be representative of a smart phone,a computer system, PDA, or other electronic devices. Examples ofcomputing systems, environments, and/or configurations that mayrepresented by the data processing system 402, 404 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, network PCs, minicomputersystems, and distributed cloud computing environments that include anyof the above systems or devices.

The client computing device 102 and the server 112 may includerespective sets of internal components 402 a,b and external components404 a,b illustrated in FIG. 4. Each of the sets of internal components402 include one or more processors 420, one or more computer-readableRAMs 422, and one or more computer-readable ROMs 424 on one or morebuses 426, and one or more operating systems 428 and one or morecomputer-readable tangible storage devices 430. The one or moreoperating systems 428, the software program 108 and the alternatekeyword recommendation program 110A in the client computing device 102,and the alternate keyword recommendation program 110B in the server 112are stored on one or more of the respective computer-readable tangiblestorage devices 430 for execution by one or more of the respectiveprocessors 420 via one or more of the respective RAMs 422 (whichtypically include cache memory). In the embodiment illustrated in FIG.4, each of the computer-readable tangible storage devices 430 is amagnetic disk storage device of an internal hard drive. Alternatively,each of the computer-readable tangible storage devices 430 is asemiconductor storage device such as ROM 424, EPROM, flash memory or anyother computer-readable tangible storage device that can store acomputer program and digital information.

Each set of internal components 402 a,b also includes a R/W drive orinterface 432 to read from and write to one or more portablecomputer-readable tangible storage devices 438 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the alternatekeyword recommendation program 110A, 110B, can be stored on one or moreof the respective portable computer-readable tangible storage devices438, read via the respective R/W drive or interface 432, and loaded intothe respective hard drive 430.

Each set of internal components 402 a,b also includes network adaptersor interfaces 436 such as a TCP/IP adapter cards, wireless Wi-Fiinterface cards, or 3G or 4G wireless interface cards or other wired orwireless communication links. The software program 108 and the alternatekeyword recommendation program 110A in the client computing device 102and the alternate keyword recommendation program 110B in the server 112can be downloaded to the client computing device 102 and the server 112from an external computer via a network (for example, the Internet, alocal area network or other, wide area network) and respective networkadapters or interfaces 436. From the network adapters or interfaces 436,the software program 108 and alternate keyword recommendation program110A in the client computing device 102 and the alternate keywordrecommendation program 110B in the server 112 are loaded into therespective hard drive 430. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 404 a,b can include a computerdisplay monitor 444, a keyboard 442, and a computer mouse 434. Externalcomponents 404 a,b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 402 a,b also includes device drivers 440to interface to computer display monitor 444, keyboard 442, and computermouse 434. The device drivers 440, R/W drive or interface 432, andnetwork adapter or interface 436 comprise hardware and software (storedin storage device 430 and/or ROM 424).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 100 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 100 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes100 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6 a set of functional abstraction layers 600provided by cloud computing environment 50 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and alternate keyword recommendation 96.Alternate keyword recommendation 96 may relate to calculating acontribution value for each user-entered keyword in a search query andcorrelation score for alternate keywords to use in the user-enteredkeyword sentence. Additionally, alternate keyword recommendation 96 mayinclude displaying statistical data to a user along with relevantalternate keyword searches.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A processor-implemented method for improving dataprocessing of a plurality of user-entered search text by recommending aplurality of alternate search keywords, the method comprising:executing, by a processor, a first search query using the plurality ofuser-entered search text, wherein the first search query is based on akeyword group identified from the plurality of user-entered search textusing an automatic summarization; identifying a highest contributionkeyword from the keyword group based on a plurality of search results ofthe executed first search query, wherein identifying the highestcontribution keyword based on calculating a contribution value of eachkeyword from the executed first search query to a plurality of searchresults, wherein the contribution value is calculated using an equation(1+log(numDocs/docfreq+1)), wherein numDocs is a number of total searchresult hits, and wherein docfreq is a number of documents within thesearch results within which a particular keyword appears; identifying ahighest correlation alternate keyword to the identified highestcontribution keyword; creating an alternate keyword group from thekeyword group by replacing the identified highest contribution keywordwith the identified highest correlation alternate keyword; executing asecond search query using the created alternate keyword group; anddisplaying, using a graphical user interface, the plurality of searchresults associated with the executed first search query and theplurality of search results associated with the executed second searchquery coupled with a plurality of statistics associated with theexecuted second search query.
 2. The method of claim 1, wherein theplurality of user-entered search text is selected from a groupconsisting of a search sentence and a plurality of keywords, and furthercomprising: in response to the plurality of user-entered search textbeing a search sentence, identifying a plurality of keywords associatedwith the search sentence using at least one natural language processingtechnique.
 3. The method of claim 1, wherein identifying the highestcorrelation alternate keyword further comprises: identifying a pluralityof alternate keywords closely related to or synonymous with theidentified highest contribution keyword; and calculating a correlationvalue for each alternate keyword within the identified plurality ofalternate keywords.
 4. The method of claim 3, wherein the correlationvalue is a comparison between a measured value of a number ofoccurrences of a keyword in a search result set and an expected numberof occurrences of the keyword in a refined portion of the search resultset.
 5. The method of claim 1, wherein the plurality of statistics isselected from a group consisting of a degree of approximation and amultiplicity value.
 6. The method of claim 5, wherein the degree ofapproximation is a comparison of a correlation between an originalkeyword group used to execute the first search query and the createdalternate keyword group used to execute the second search query.
 7. Themethod of claim 5, wherein the multiplicity value is a valuerepresenting a similarity between the plurality of search results of theexecuted first search query and a plurality of search results of theexecuted second search query.
 8. A computer system for improving dataprocessing of a plurality of user-entered search text by recommending aplurality of alternate search keywords, the computer system comprising:one or more processors, one or more computer-readable memories, one ormore computer-readable tangible storage medium, and program instructionsstored on at least one of the one or more tangible storage medium forexecution by at least one of the one or more processors via at least oneof the one or more memories, wherein the computer system is capable ofperforming a method comprising: executing a first search query using theplurality of user-entered search text, wherein the first search query isbased on a keyword group identified from the plurality of user-enteredsearch text using an automatic summarization; identifying a highestcontribution keyword from the keyword group based on a plurality ofsearch results of the executed first search query, wherein identifyingthe highest contribution keyword based on calculating a contributionvalue of each keyword from the executed first search query to aplurality of search results, wherein the contribution value iscalculated using an equation (1+log(numDocs/docfreq+1)), wherein numDocsis a number of total search result hits, and wherein docfreq is a numberof documents within the search results within which a particular keywordappears; identifying a highest correlation alternate keyword to theidentified highest contribution keyword; creating an alternate keywordgroup from the keyword group by replacing the identified highestcontribution keyword with the identified highest correlation alternatekeyword; executing a second search query using the created alternatekeyword group; and displaying, using a graphical user interface, theplurality of search results associated with the executed first searchquery and the plurality of search results associated with the executedsecond search query coupled with a plurality of statistics associatedwith the executed second search query.
 9. The computer system of claim8, wherein the plurality of user-entered search text is selected from agroup consisting of a search sentence and a plurality of keywords, andfurther comprising: in response to the plurality of user-entered searchtext being a search sentence, identifying a plurality of keywordsassociated with the search sentence using at least one natural languageprocessing technique.
 10. The computer system of claim 8, whereinidentifying the highest correlation alternate keyword further comprises:identifying a plurality of alternate keywords closely related to orsynonymous with the identified highest contribution keyword; andcalculating a correlation value for each alternate keyword within theidentified plurality of alternate keywords.
 11. The computer system ofclaim 10, wherein the correlation value is a comparison between ameasured value of a number of occurrences of a keyword in a searchresult set and an expected number of occurrences of the keyword in arefined portion of the search result set.
 12. The computer system ofclaim 8, wherein the plurality of statistics is selected from a groupconsisting of a degree of approximation and a multiplicity value. 13.The computer system of claim 12, wherein the degree of approximation isa comparison of a correlation between an original keyword group used toexecute the first search query and the created alternate keyword groupused to execute the second search query.
 14. The computer system ofclaim 12, wherein the multiplicity value is a value representing asimilarity between the plurality of search results of the executed firstsearch query and a plurality of search results of the executed secondsearch query.
 15. A computer program product for improving dataprocessing of a plurality of user-entered search text by recommending aplurality of alternate search keywords, the computer program productcomprising: one or more computer-readable tangible storage medium andprogram instructions stored on at least one of the one or more tangiblestorage medium, the program instructions executable by a processor, theprogram instructions comprising: program instructions to execute a firstsearch query using the plurality of user-entered search text, whereinthe first search query is based on a keyword group identified from theplurality of user-entered search text using an automatic summarization;program instructions to identify a highest contribution keyword from thekeyword group based on a plurality of search results of the executedfirst search query, wherein program instructions to identify the highestcontribution keyword are based on calculating a contribution value ofeach keyword from the executed first search query to a plurality ofsearch results, wherein the contribution value is calculated using anequation (1+log(numDocs/docfreq+1)), wherein numDocs is a number oftotal search result hits, and wherein docfreq is a number of documentswithin the search results within which a particular keyword appears;program instructions to identify a highest correlation alternate keywordto the identified highest contribution keyword; program instructions tocreate an alternate keyword group from the keyword group by replacingthe identified highest contribution keyword with the identified highestcorrelation alternate keyword; program instructions to execute a secondsearch query using the created alternate keyword group; and programinstructions to display, using a graphical user interface, the pluralityof search results associated with the executed first search query andthe plurality of search results associated with the executed secondsearch query coupled with a plurality of statistics associated with theexecuted second search query.
 16. The computer program product of claim15, wherein the plurality of user-entered search text is selected from agroup consisting of a search sentence and a plurality of keywords, andfurther comprising: in response to the plurality of user-entered searchtext being a search sentence, program instructions to identify aplurality of keywords associated with the search sentence using at leastone natural language processing technique.